Retinoid X receptor promotes hematopoietic stem cell fitness and quiescence and preserves hematopoietic homeostasis.

Hematopoietic stem cells (HSCs) balance self-renewal and differentiation to maintain hematopoietic fitness throughout life. In steady-state conditions, HSC exhaustion is prevented by the maintenance of most HSCs in a quiescent state, with cells entering the cell cycle only occasionally. HSC quiescence is regulated by retinoid and fatty-acid ligands of transcriptional factors of the nuclear retinoid X receptor (RXR) family. Herein, we show that dual deficiency for hematopoietic RXRα and RXRß induces HSC exhaustion, myeloid cell/megakaryocyte differentiation, and myeloproliferative-like disease. RXRα and RXRß maintain HSC quiescence, survival, and chromatin compaction; moreover, transcriptome changes in RXRα;RXRß-deficient HSCs include premature acquisition of an aging-like HSC signature, MYC pathway upregulation, and RNA intron retention. Fitness loss and associated RNA transcriptome and splicing alterations in RXRα;RXRß-deficient HSCs are prevented by Myc haploinsufficiency. Our study reveals the critical importance of RXRs for the maintenance of HSC fitness and their protection from premature aging.

Resolving single-cell heterogeneity from hundreds of thousands of cells through sequential hybrid clustering and NMF.

MOTIVATION: The rapid proliferation of single-cell RNA-sequencing (scRNA-Seq) technologies has spurred the development of diverse computational approaches to detect transcriptionally coherent populations. While the complexity of the algorithms for detecting heterogeneity has increased, most require significant user-tuning, are heavily reliant on dimension reduction techniques and are not scalable to ultra-large datasets. We previously described a multi-step algorithm, Iterative Clustering and Guide-gene Selection (ICGS), which applies intra-gene correlation and hybrid clustering to uniquely resolve novel transcriptionally coherent cell populations from an intuitive graphical user interface. RESULTS: We describe a new iteration of ICGS that outperforms state-of-the-art scRNA-Seq detection workflows when applied to well-established benchmarks. This approach combines multiple complementary subtype detection methods (HOPACH, sparse non-negative matrix factorization, cluster 'fitness', support vector machine) to resolve rare and common cell-states, while minimizing differences due to donor or batch effects. Using data from multiple cell atlases, we show that the PageRank algorithm effectively downsamples ultra-large scRNA-Seq datasets, without losing extremely rare or transcriptionally similar yet distinct cell types and while recovering novel transcriptionally distinct cell populations. We believe this new approach holds tremendous promise in reproducibly resolving hidden cell populations in complex datasets. AVAILABILITY AND IMPLEMENTATION: ICGS2 is implemented in Python. The source code and documentation are available at http://altanalyze.org. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Analysis of gene expression profiles of multiple skin diseases identifies a conserved signature of disrupted homeostasis.

Triggers of skin disease pathogenesis vary, but events associated with the elicitation of a lesion share many features in common. Our objective was to examine gene expression patterns in skin disease to develop a molecular signature of disruption of cutaneous homeostasis. Gene expression data from common inflammatory skin diseases (eg psoriasis, atopic dermatitis, seborrhoeic dermatitis and acne) and a novel statistical algorithm were used to define a unifying molecular signature referred to as the "unhealthy skin signature" (USS). Using a pattern-matching algorithm, analysis of public data repositories revealed that the USS is found in diverse epithelial diseases. Studies of milder disruptions of epidermal homeostasis have also shown that these conditions converge, to varying degrees, on the USS and that the degree of convergence is related directly to the severity of homeostatic disruption. The USS contains genes that had no prior published association with skin, but that play important roles in many different disease processes, supporting the importance of the USS to homeostasis. Finally, we show through pattern matching that the USS can be used to discover new potential dermatologic therapeutics. The USS provides a new means to further interrogate epithelial homeostasis and potentially develop novel therapeutics with efficacy across a spectrum of skin conditions.

MAMS: matrix and analysis metadata standards to facilitate harmonization and reproducibility of single-cell data.

Many datasets are being produced by consortia that seek to characterize healthy and disease tissues at single-cell resolution. While biospecimen and experimental information is often captured, detailed metadata standards related to data matrices and analysis workflows are currently lacking. To address this, we develop the matrix and analysis metadata standards (MAMS) to serve as a resource for data centers, repositories, and tool developers. We define metadata fields for matrices and parameters commonly utilized in analytical workflows and developed the rmams package to extract MAMS from single-cell objects. Overall, MAMS promotes the harmonization, integration, and reproducibility of single-cell data across platforms.

Matrix and analysis metadata standards (MAMS) to facilitate harmonization and reproducibility of single-cell data.

A large number of genomic and imaging datasets are being produced by consortia that seek to characterize healthy and disease tissues at single-cell resolution. While much effort has been devoted to capturing information related to biospecimen information and experimental procedures, the metadata standards that describe data matrices and the analysis workflows that produced them are relatively lacking. Detailed metadata schema related to data analysis are needed to facilitate sharing and interoperability across groups and to promote data provenance for reproducibility. To address this need, we developed the Matrix and Analysis Metadata Standards (MAMS) to serve as a resource for data coordinating centers and tool developers. We first curated several simple and complex "use cases" to characterize the types of feature-observation matrices (FOMs), annotations, and analysis metadata produced in different workflows. Based on these use cases, metadata fields were defined to describe the data contained within each matrix including those related to processing, modality, and subsets. Suggested terms were created for the majority of fields to aid in harmonization of metadata terms across groups. Additional provenance metadata fields were also defined to describe the software and workflows that produced each FOM. Finally, we developed a simple list-like schema that can be used to store MAMS information and implemented in multiple formats. Overall, MAMS can be used as a guide to harmonize analysis-related metadata which will ultimately facilitate integration of datasets across tools and consortia. MAMS specifications, use cases, and examples can be found at https://github.com/single-cell-mams/mams/.

Fetal maturation revealed by amniotic fluid cell-free transcriptome in rhesus macaques.

Accurate estimate of fetal maturity could provide individualized guidance for delivery of complicated pregnancies. However, current methods are invasive, have low accuracy, and are limited to fetal lung maturation. To identify diagnostic gestational biomarkers, we performed transcriptomic profiling of lung and brain, as well as cell-free RNA from amniotic fluid of preterm and term rhesus macaque fetuses. These data identify potentially new and prior-associated gestational age differences in distinct lung and neuronal cell populations when compared with existing single-cell and bulk RNA-Seq data. Comparative analyses found hundreds of genes coincidently induced in lung and amniotic fluid, along with dozens in brain and amniotic fluid. These data enable creation of computational models that accurately predict lung compliance from amniotic fluid and lung transcriptome of preterm fetuses treated with antenatal corticosteroids. Importantly, antenatal steroids induced off-target gene expression changes in the brain, impinging upon synaptic transmission and neuronal and glial maturation, as this could have long-term consequences on brain development. Cell-free RNA in amniotic fluid may provide a substrate of global fetal maturation markers for personalized management of at-risk pregnancies.

DoubletDecon: Deconvoluting Doublets from Single-Cell RNA-Sequencing Data.

Methods for single-cell RNA sequencing (scRNA-seq) have greatly advanced in recent years. While droplet- and well-based methods have increased the capture frequency of cells for scRNA-seq, these technologies readily produce technical artifacts, such as doublet cell captures. Doublets occurring between distinct cell types can appear as hybrid scRNA-seq profiles, but do not have distinct transcriptomes from individual cell states. We introduce DoubletDecon, an approach that detects doublets with a combination of deconvolution analyses and the identification of unique cell-state gene expression. We demonstrate the ability of DoubletDecon to identify synthetic, mixed-species, genetic, and cell-hashing cell doublets from scRNA-seq datasets of varying cellular complexity with a high sensitivity relative to alternative approaches. Importantly, this algorithm prevents the prediction of valid mixed-lineage and transitional cell states as doublets by considering their unique gene expression. DoubletDecon has an easy-to-use graphical user interface and is compatible with diverse species and unsupervised population detection algorithms.

Asymptomatic or "silent" atrial fibrillation: frequency in untreated patients and patients receiving azimilide.

BACKGROUND: Asymptomatic, or "silent" atrial fibrillation could increase the risk of stroke. Little is known about the frequency of asymptomatic atrial fibrillation in patients who also have symptomatic atrial fibrillation; similarly, little is known about the effect of antiarrhythmic drug therapy on asymptomatic atrial fibrillation. METHODS AND RESULTS: Patients in sinus rhythm with a history of symptomatic atrial fibrillation or atrial flutter received placebo or azimilide (35 to 125 mg) once daily for 6 or 9 months in 4 similar double-blind trials. The end point was the first recurrence of a symptomatic ECG-documented supraventricular arrhythmia. Routine transtelephonic electrocardiograms, in the absence of symptoms, were recorded for 30 seconds every 2 weeks until patients completed follow-up or documented a symptomatic supraventricular arrhythmia. Of the 1380 patients, 489 received placebo. Among these patients receiving placebo, 303 transmitted at least one routine ECG while asymptomatic. Asymptomatic atrial fibrillation was recorded in 50 (17%) within 6 months and before recurrence of symptomatic supraventricular arrhythmia. In the 3 trials evaluating azimilide in therapeutic doses (100 and 125 mg), asymptomatic atrial fibrillation occurred in 49 of 382 (13%) receiving azimilide and 43 of 233 (18%) receiving placebo. Although drug effect on time to first asymptomatic event was not statistically significant (hazard ratio, 0.70; P=0.09), there was a 40% reduction in asymptomatic atrial fibrillation on azimilide compared with placebo (P=0.03) when repeated observations were considered. CONCLUSIONS: Asymptomatic atrial fibrillation is common in untreated patients with a history of symptomatic atrial fibrillation (and is likely underestimated by this analysis). Azimilide may reduce the occurrence of this silent arrhythmia.

Antiarrhythmic effects of azimilide in paroxysmal supraventricular tachycardia: efficacy and dose-response.

BACKGROUND: Azimilide is a novel classification III antiarrhythmic agent that blocks both I(Kr)and I(Ks)and shows evidence of efficacy in patients with atrial fibrillation or flutter. Its effect on paroxysmal supraventricular tachycardia (PSVT) has not been reported. METHODS AND RESULTS: One hundred ninety-three patients with symptomatic PSVT were enrolled in 4 double-blind, randomized, placebo-controlled clinical trials with almost identical trial design (supraventricular arrhythmia-1 [SVA-1], SVA-2, SVA-3, and SVA-4), performed as a separate stratum of studies that also included a stratum of patients with atrial fibrillation or flutter. Patients received oral azimilide (100 mg in SVA-1 and SVA-3, 35 or 75 mg in SVA-2, and 125 mg in SVA-4) or matching placebo twice daily for 3 days (loading period), followed by once-daily dosing (maintenance period). The primary outcome variable was the first recording of a symptomatic supraventricular arrhythmia (either PSVT, atrial fibrillation, or atrial flutter) recorded on a transtelephonic electrocardiographic event recorder. The duration of study was 270 days in SVA-1 and SVA-2 and 180 days in SVA-3 and SVA-4. Combined analysis results of the PSVT stratum from the 4 studies showed a dose-response relationship in prolongation of time to recurrence (P =.02). In the combined 100-mg daily dose, the hazard ratio (placebo:azimilide) was 2.35 (P =.023). The hazard ratio for the 125-mg daily dose measured 1.28 (P = not significant). However, the time to recurrence was prolonged when the patients receiving 100 and 125 mg daily were combined and compared with placebo (P =.02). There were no deaths and 1 case of torsades de pointes. CONCLUSION: These trial results suggest a significant dose-related suppression of PSVT with azimilide, with a low risk of serious adverse events.

Symptoms at the time of arrhythmia recurrence in patients receiving azimilide for control of atrial fibrillation or flutter: results from randomized trials.

BACKGROUND: Azimilide is a new antiarrhythmic agent being developed for the management for atrial fibrillation and flutter (AF). Four randomized, placebo-controlled, double-blind trials have been performed that investigated the effect of azimilide on time to first recurrence of symptomatic AF. This paper examines the data collected during those studies regarding the symptoms reported by patients at the time of AF recurrence METHODS: At the time that patients reported their first documented symptomatic recurrence of arrhythmia, they were systematically asked whether or not they were experiencing any of the following 6 symptoms: palpitation, fatigue, chest pain, shortness of breath, dizziness, or sweating. Patients were required to answer yes or no. A symptom score was created varying from 0 to 6, in increasing order of number of symptoms reported. This was compared for patients receiving either of 2 doses of azimilide or placebo. The relationship between the number of symptoms, heart rate at time of arrhythmia recurrence and treatment was analyzed. RESULTS: In 2 separate studies, azimilide at a dose of 125 mg/day significantly reduced the number of symptoms at the time of arrhythmia recurrence compared to placebo. On the other hand, in 2 studies, the dose of 100 mg/day did not significantly reduce symptom burden. The individual symptoms significantly reduced by azimilide125 mg/day were fatigue, shortness of breath, chest pain and dizziness. Palpitations and sweating were not significantly reduced. Modeling of heart rate at the time of arrhythmia recurrence, symptoms and treatment indicated that a small reduction in heart rate with azimilide accounted for only a small part of the symptom reduction. There was another effect of azimilide: an average reduction of 0.38 symptoms (P <.01) that was independent of heart rate. CONCLUSION: Azimilide (125 mg/day) reduces the number of symptoms reported at the time of AF recurrence.